Edits for presentation

HeidiSteen · HeidiSteen · commit 386546bb8663 · 2024-06-12T15:59:54.000-07:00
diff --git a/articles/search/hybrid-search-how-to-query.md b/articles/search/hybrid-search-how-to-query.md
@@ -18,7 +18,7 @@ ms.date: 06/12/2024
 
 In most cases, [per benchmark tests](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167), hybrid queries with semantic ranking return the most relevant results.
 
-New parameters [maxTextRecallSize and countAndFacetMode(preview)](#relevance-tuning-through-maxtextrecallsize-and-countandfacetmode-preview) give you more control over inputs into a hybrid query. You can also use [vector weighting](vector-search-how-to-query.md#vector-weighting-preview) on the vector side of a hybrid query to specify the relative weight of the vector query. This feature is particularly useful in complex queries where two or more distinct result sets need to be combined, as is the case for hybrid search. 
+New parameters [maxTextRecallSize and countAndFacetMode(preview)](#set-maxtextrecallsize-and-countandfacetmode-preview) give you more control over inputs into a hybrid query. You can also use [vector weighting](vector-search-how-to-query.md#vector-weighting-preview) on the vector side of a hybrid query to specify the relative weight of the vector query. This feature is particularly useful in complex queries where two or more distinct result sets need to be combined, as is the case for hybrid search. 
 
 ## Prerequisites
 
@@ -233,21 +233,21 @@ api-key: {{admin-api-key}}
 
 + Postfilter is applied after query execution. If k=50 returns 50 matches on the vector query side, then the post-filter is applied to the 50 matches, reducing results that meet filter criteria, leaving you with fewer than 50 documents to pass to semantic ranker.
 
-## Relevance tuning through maxTextRecallSize and countAndFacetMode (preview)
+## Set maxTextRecallSize and countAndFacetMode (preview)
 
 In a `hybridSearch` query parameter object, specify the maximum number of documents recalled through the text portion of a hybrid (text and vector) query. You can also set `countAndFacetMode` property to report the counts for the text query (and for facets if you're using them) to the document limits set by `maxTextRecallSize`.
 
 The default maximum is `1000` documents. 
 
-With `maxTextRecallSize`, you can increase or decrease the number of BM25-ranked results returned in hybrid queries. The default is 1,000. The maximum is 10,000.
+With `maxTextRecallSize`, you can increase or decrease the number of BM25-ranked results returned in hybrid queries. The default is `1000`. The maximum is `10000`.
 
-+ Reducing the number of text documents retrieved can significantly improve performance, assuming vector similarity search is generally performing better. You can also consider [vector weighting](vector-search-how-to-query.md#vector-weighting-preview) as an alternate approach for increasing the importance of vector queries.
++ Reducing `maxTextRecallSize` below the default an significantly improve performance, assuming vector similarity search is generally performing better. You can also consider [vector weighting](vector-search-how-to-query.md#vector-weighting-preview) as an alternate approach for increasing the importance of vector queries.
 
-+ Increasing the number of text documents is useful if you have a large index, and the `1000` document default is not capturing a sufficient number of results. With a larger BM25-ranked result set, you can also set `top`, `skip`, and `next` to retrieve portions of those results.
++ Increasing `maxTextRecallSize` is useful if you have a large index, and the default isn't capturing a sufficient number of results. With a larger BM25-ranked result set, you can also set `top`, `skip`, and `next` to retrieve portions of those results.
 
 Use [Search - POST](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true) or [Search - GET](/rest/api/searchservice/documents/search-get?view=rest-searchservice-2024-05-01-preview&preserve-view=true) in `2024-05-01-Preview` to specify these parameters.
 
-The following example sets `maxTextRecallSize` to 100, which limits the results from the text side of the hybrid query to just 100 documents instead of all matches (up to 1,000 document default). It also sets `countAndFacetMode` to include only those results from `maxTextRecallSize`.
+The first example lowers `maxTextRecallSize` to 100, limiting the text side of the hybrid query to just 100 document. It also sets `countAndFacetMode` to include only those results from `maxTextRecallSize`.
 
 ```http
 POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2024-05-01-Preview 
@@ -269,7 +269,7 @@ POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?
     } 
 ```
 
-The next example sets `maxTextRecallSize` to 5,000. It also uses top, skip, and next to pull results from large result sets. In this case, the request pulls 500 matches starting at position 1,500 through 2,000.
+The second example raises `maxTextRecallSize` to 5,000. It also uses top, skip, and next to pull results from large result sets. In this case, the request pulls in BM25-ranked results starting at position 1,500 through 2,000 as the text query contribution to the RRF composite result set.
 
 ```http
 POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2024-05-01-Preview 
diff --git a/articles/search/search-pagination-page-layout.md b/articles/search/search-pagination-page-layout.md
@@ -68,7 +68,7 @@ Count won't be affected by routine maintenance or other workloads on the search
 
 By default, the search engine returns up to the first 50 matches. The top 50 are determined by search score, assuming the query is full text search or semantic. Otherwise, the top 50 are an arbitrary order for exact match queries (where uniform "@searchScore=1.0" indicates arbitrary ranking).
 
-The upper limit is 1,000 documents returned per page of search results, so you can set top to return up to 1000 document in the first result. In newer preview APIs, if you're using a hybrid query, you can [specify maxTextRecallSize](hybrid-search-how-to-query.md#relevance-tuning-through-maxtextrecallsize-and-countandfacetmode-preview) to return up to 10,000 documents.
+The upper limit is 1,000 documents returned per page of search results, so you can set top to return up to 1000 document in the first result. In newer preview APIs, if you're using a hybrid query, you can [specify maxTextRecallSize](hybrid-search-how-to-query.md#set-maxtextrecallsize-and-countandfacetmode-preview) to return up to 10,000 documents.
 
 To control the paging of all documents returned in a result set, add `$top` and `$skip` parameters to the GET query request, or `top` and `skip` to the POST query request. The following list explains the logic.
 
diff --git a/articles/search/vector-search-how-to-query.md b/articles/search/vector-search-how-to-query.md
@@ -638,7 +638,7 @@ Weights are used when calculating the [reciprocal rank fusion](hybrid-search-ran
 
 The following example is a hybrid query with two vector query strings and one text string. Weights are assigned to the vector queries. The first query is 0.5 or half the weight, reducing its importance in the request. The second vectory query is twice as important. 
 
-Text queries have no weight parameters, but you can increase or decrease their importance by setting [maxTextRecallSize](hybrid-search-how-to-query.md#ranking).
+Text queries have no weight parameters, but you can increase or decrease their importance by setting [maxTextRecallSize](hybrid-search-how-to-query.md#set-maxtextrecallsize-and-countandfacetmode-preview).
 
 ```http
 POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2024-05-01-Preview 
diff --git a/articles/search/whats-new.md b/articles/search/whats-new.md
@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: overview
-ms.date: 05/31/2024
+ms.date: 06/12/2024
 ms.custom:
   - references_regions
 ---
@@ -25,7 +25,7 @@ ms.custom:
 |-----------------------------|------|--------------|
 | [Higher capacity and more vector quota at every tier (same billing rate)](search-limits-quotas-capacity.md#service-limits) | Infrastructure | Partition sizes are now even larger for Standard 2 (S2), Standard 3 (S3), and Standard 3 High Density (S3 HD) for all services created after April 3, 2024. If you create a new service now, you get the larger partitions. If you created a new service between April 3 and May 17, you get the larger partitions automatically. <br><br>Storage Optimized tiers (L1 and L2) also have more capacity. L1 and L2 customers must create a new service to benefit from the higher capacity. There's no in-place upgrade at this time. <br><br>Extra capacity is now available in [more regions](search-limits-quotas-capacity.md#supported-regions-with-higher-storage-limits): Germany North​, Germany West Central​, South Africa North​, Switzerland West​, and Azure Government (Texas, Arizona, and Virginia).|
 | [OneLake integration (preview)](search-how-to-index-onelake-files.md) | Feature | New indexer for OneLake files and OneLake shortcuts. If you use Microsoft Fabric and OneLake for data access to Amazon Web Services (AWS) and Google data sources, use this indexer to import external data into a search index. This indexer is available through the Azure portal, the [2024-05-01-preview REST API](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true), and Azure SDK beta packages. |
-| [Vector results customization](vector-search-how-to-query.md) and [hybrid query optimizations](hybrid-search-how-to-query.md) | Feature | Three enhancements improve vector search relevance. <br><br>First, you can now set thresholds on vector search results to exclude low-scoring results. <br><br>Second, you can set `MaxTextRecallSize` and `countAndFacetMode` in hybrid queries to specify the maximum number of documents that can be recalled using text query in hybrid (text and vector) search. Previously, the maximum was fixed at `1000`, and the default is still `1000`. However, if you have more matches, you can now specify a higher limit on top and skip to get more results back. <br><br>Third, for hybrid queries queries, you can set a weight on vector queries to have more or less importance than the nonvector query. |
+| [Vector results customization](vector-search-how-to-query.md) and [hybrid query relevance tuning](hybrid-search-how-to-query.md) | Feature | Three enhancements improve vector search relevance. <br><br>First, you can now set thresholds on vector search results to exclude low-scoring results. <br><br>Second, you can set `MaxTextRecallSize` and `countAndFacetMode` in hybrid queries to specify the maximum number of documents that can be recalled using text query in hybrid (text and vector) search. Previously, the maximum was fixed at `1000`, and the default is still `1000`. However, if you have more matches, you can now specify a higher limit on top and skip to get more results back. <br><br>Third, for hybrid queries queries, you can set a weight on vector queries to have more or less importance than the nonvector query. |
 | [Binary vectors support](/rest/api/searchservice/supported-data-types) | Feature | `Collection(Edm.Byte)` is a new supported data type. This data type opens up integration with the [Cohere v3 binary embedding models](https://cohere.com/blog/int8-binary-embeddings) and custom binary quantization. Narrow data types lower the cost of large vector datasets. See [Index binary data for vector search](vector-search-how-to-index-binary-data.md) for more information.| 
 | [Azure AI Vision multimodal embeddings skill (preview)](cognitive-search-skill-vision-vectorize.md) | Skill | New skill that's bound to the [multimodal embeddings API of Azure AI Vision](../ai-services/computer-vision/concept-image-retrieval.md). You can generate embeddings for text or images during indexing. This skill is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
 | [Azure AI Vision vectorizer (preview)](vector-search-vectorizer-ai-services-vision.md) | Vectorizer | New vectorizer connects to an Azure AI Vision resource using the [multimodal embeddings API](../ai-services/computer-vision/concept-image-retrieval.md) to generate embeddings at query time. This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
